Histogram of Neighborhood Tripartite Authentication with Fingerprint-based Biometrics for IoT Services

Abstract

Internet of Things (IoT) and services is an interesting topic with a wide range of potential applications like smart home systems, health care, telemedicine, and intelligent transportation. Traditionally, key agreement schemes have been evaluated to access IoT services which are highly susceptible to security. Recently, Biometric-based authentication is also used to access IoT services and devices. They are involving a larger amount of memory with increased running time and found to be computationally infeasible. To provide robust authentication for IoT services, Histogram of Neighborhood Tripartite Authentication with Fingerprint Biometrics (HNTA-FB) for IoT services is proposed in this paper. This proposed HNTA-FB method uses binary patterns and a histogram of features to extract the region of interest. To reduce the memory requirements while providing access to IoT services, Histogram of Neighborhood Binary Pattern Pre-processing (HNBPP) model is proposed. The discriminative power of Neighbourhood Binary Pattern Registration (NBPR) is integrated with the normalized sparse representation based on the histogram. Additionally, this work presents a new Tripartite User Authentication model for fingerprint biometric template matching process. When compared with different state-of-the-art methods, the proposed method depicts significantly improved performance in terms of matching accuracy, computational overhead and execution speed and is highly effective in delivering smart home services.

IoT Integration in Telemedicine: Investigating the Role of Internet of Things Devices in Facilitating Remote Patient Monitoring and Data Transmission

“…The foundation of IoT-enabled telemedicine hinges on real-time data transmission and analysis, where IoT devices like remote sensors and home medical equipment act as diligent data collectors, continuously gathering patients' health information . This data ows seamlessly to healthcare providers and centralized systems for rapid analysis and interpretation (Kanchana, 2019).…”